#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
# @FileName  :mnist-lenet.py
# @Time      :2024/3/25 15:41
# @Author    :Toliong toliong@foxmail.com
"""
import paddle
import numpy as np
import matplotlib.pyplot as plt
import paddle.nn.functional as F
from paddle.vision.transforms import Compose, Normalize
from paddle.metric import Accuracy

"""
1. 数据加载
手写数字的MNIST数据集，包含60,000个用于训练的示例和10,000个用于测试的示例。
这些数字已经过尺寸标准化并位于图像中心，图像是固定大小(28x28像素)，其值为0到1。
该数据集的官方地址为：http://yann.lecun.com/exdb/mnist 。
"""

transform = Compose([Normalize(mean=[127.7],
                               std=[127.5],
                               data_format='CHW')])

# 使用transform对数据集做归一化
print('download training data and load training data')
train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)
test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=transform)
print('{} images in train_dataset, {} images in test_dataset'.format(len(train_dataset), len(test_dataset)))
print('load finished')

"""
取训练集中的一条数据看一下 
"""
train_data0, train_label_0 = train_dataset[0][0], train_dataset[0][1]
train_data0 = train_data0.reshape([28, 28])
plt.figure(figsize=(2, 2))
plt.imshow(train_data0, cmap=plt.cm.binary)
print('train_data0 label is: ' + str(train_label_0))

"""
2. 组网
用paddle.nn下的API，如Conv2D、MaxPool2D、Linear完成LeNet的构建。
"""


class LeNet(paddle.nn.Layer):
    def __init__(self):
        super().__init__()
        self.conv1 = paddle.nn.Conv2D(in_channels=1, out_channels=6, kernel_size=5, stride=1, padding=2)
        self.max_pool1 = paddle.nn.MaxPool2D(kernel_size=2, stride=2)
        self.conv2 = paddle.nn.Conv2D(in_channels=6, out_channels=16, kernel_size=5, stride=1)
        self.max_pool2 = paddle.nn.MaxPool2D(kernel_size=2, stride=2)
        self.linear1 = paddle.nn.Linear(in_features=16 * 5 * 5, out_features=120)
        self.linear2 = paddle.nn.Linear(in_features=120, out_features=84)
        self.linear3 = paddle.nn.Linear(in_features=84, out_features=10)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x = self.max_pool1(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = self.max_pool2(x)
        x = paddle.flatten(x, start_axis=1, stop_axis=-1)
        x = self.linear1(x)
        x = F.relu(x)
        x = self.linear2(x)
        x = F.relu(x)
        x = self.linear3(x)
        return x



"""
3、基于基础API，完成模型的训练与预测

3.1 模型训练
组网后，开始对模型进行训练，先构建train_loader，加载训练数据，然后定义train函数，设置好损失函数后，按batch加载数据，完成模型的训练。
"""
train_loader = paddle.io.DataLoader(train_dataset, batch_size=64, shuffle=True)


# 加载训练集 batch_size 设为 64
def train5(model):
    model.train()
    epochs = 2
    optim = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())
    # 用Adam作为优化函数
    for epoch in range(epochs):
        for batch_id, data in enumerate(train_loader()):
            x_data = data[0]
            y_data = data[1]
            predicts = model(x_data)
            loss = F.cross_entropy(predicts, y_data)
            # 计算损失
            acc = paddle.metric.accuracy(predicts, y_data)
            loss.backward()
            if batch_id % 300 == 0:
                print("epoch: {}, batch_id: {}, loss is: {}, acc is: {}".format(epoch, batch_id, loss.numpy(),
                                                                                acc.numpy()))
            optim.step()
            optim.clear_grad()


model5 = LeNet()
train5(model5)

"""
3.2 验证模型
"""

test_loader = paddle.io.DataLoader(test_dataset, places=paddle.CPUPlace(), batch_size=64)


# 加载测试数据集
def test(model):
    model.eval()
    batch_size = 64
    for batch_id, data in enumerate(test_loader()):
        x_data = data[0]
        y_data = data[1]
        predicts = model(x_data)
        # 获取预测结果
        loss = F.cross_entropy(predicts, y_data)
        acc = paddle.metric.accuracy(predicts, y_data)
        if batch_id % 20 == 0:
            print("batch_id: {}, loss is: {}, acc is: {}".format(batch_id, loss.numpy(), acc.numpy()))


test(model5)
